
import pandas as pd
import numpy as np
np.random.seed(0)
import matplotlib.pyplot as plt
import seaborn as sb

from sklearn.preprocessing import OneHotEncoder, StandardScaler
from sklearn.model_selection import train_test_split
from sklearn.metrics import mean_squared_error, mean_absolute_error
from sklearn import preprocessing
from sklearn.model_selection import train_test_split, GridSearchCV
import xgboost as xgb

import shap
import time
import warnings

Using `tqdm.autonotebook.tqdm` in notebook mode. Use `tqdm.tqdm` instead to force console mode (e.g. in jupyter console)


df = pd.read_csv('../data/day-bikesharing.csv',parse_dates=['dteday'],index_col=1) # Load the data


def add_features(df):
    df['year'] = df.index.year # yr exists already
    df['month'] = df.index.month
    df['day'] = df.index.day
    df['dayofweek'] = df.index.dayofweek


add_features(df)
df.head(3)


# Let's use 'dayofweek' instead of 'weekday'.
plt.figure(figsize=(16,3))
df['weekday'][:7].plot(linestyle='--', marker='o', color='k', ms=20, 
                       title='weekday feature (confusing with \'6\' and \'0\' for weekends)')
plt.show()


plt.figure(figsize=(16,3))
df['dayofweek'][:14].plot(linestyle='--', marker='o', color='r', ms=20, 
                       title='dayofweek feature')
plt.show()


# Day feature shows whether it is a start of the month or the end of the month (1 to 30 or 31).
plt.figure(figsize=(16,3))
df['day'][:33].plot(linestyle='--', marker='o', color='c', ms=20, title='day feature')
plt.show()


plt.figure(figsize=(16,3))
df['workingday'][:7].plot(linestyle='--', marker='o', color='b', ms=20, title='workingday feature')
plt.show()


plt.figure(figsize=(16,5))
df['season'].plot()
plt.show()


df.season.value_counts()

3    188
2    184
1    181
4    178
Name: season, dtype: int64


plt.figure(figsize=(16,5))
df['cnt'].plot()
plt.show()


plt.scatter(x=df.temp*41,y=df["cnt"]) # denormalize temperature by multiplying 41
plt.grid(True)
plt.xlabel('Temperature')
plt.ylabel('Rental Count')
plt.title('Temperature vs. Rental Count')
plt.show()


# from January to April, the demand is relatively low due to Winter 
#(there are not a lot of people that's brave enough to bike on icy road, are there?). 
# And the peak demand happends around Summer time (from May to October) and drop again.
group_month = df.groupby(['month'])
average_by_month = group_month['cnt'].mean()

plt.plot(average_by_month.index,average_by_month)
plt.xlabel('Month')
plt.ylabel('Count')
plt.xticks(np.arange(13))
plt.grid(True)
plt.title('Average Rental Count by Month')

Text(0.5, 1.0, 'Average Rental Count by Month')


#correlation of total cnt with other variables
plt.figure(figsize=(16,5))
df.corr()['cnt'].plot(kind='bar')
plt.show()


#This is the normalized version.
# temp: The values are divided to 41 (max)
# atemp: The values are divided to 50 (max)
# hum: The values are divided to 100 (max)
# windspeed: The values are divided to 67 (max)
df.describe().T


df.dtypes

instant         int64
season          int64
yr              int64
mnth            int64
holiday         int64
weekday         int64
workingday      int64
weathersit      int64
temp          float64
atemp         float64
hum           float64
windspeed     float64
casual          int64
registered      int64
cnt             int64
year            int64
month           int64
day             int64
dayofweek       int64
dtype: object


# Define the categorical variables
cat_var = ['season', 'holiday', 'workingday', 'weathersit', 'year']


# OneHotEncoder
ohe = OneHotEncoder(categories = 'auto')


# Fit the categorical variables to the encoder
encodeddf = ohe.fit_transform(df[cat_var])


# Create a DataFrame with the encoded value information
cat_df = pd.DataFrame(data = encodeddf.toarray(), columns = ohe.get_feature_names_out(cat_var))
cat_df


# rename the column names from 1,2,3,4 to spring, summer, fall, winter
cat_df.rename(columns={'season_1': 'winter', 'season_2': 'spring', 'season_3': 'summer', 'season_4': 'fall'}, inplace=True)
cat_df.rename(columns={'weathersit_1': 'clear', 'weathersit_2': 'cloudy', 'weathersit_3': 'lightsnow.rain'}, inplace=True)
cat_df


# Define the numerical variables
num_var = ['temp', 'atemp', 'hum', 'windspeed', 'month', 'day', 'dayofweek']


# StandardScaler object
scaler = StandardScaler()


# Fit the data to the scaler
numscaled = scaler.fit_transform(df[num_var])


# for inverse transformation
inversed = scaler.inverse_transform(numscaled)
print(inversed)

[[ 0.344167  0.363625  0.805833 ...  1.        1.        5.      ]
 [ 0.363478  0.353739  0.696087 ...  1.        2.        6.      ]
 [ 0.196364  0.189405  0.437273 ...  1.        3.        0.      ]
 ...
 [ 0.253333  0.2424    0.752917 ... 12.       29.        5.      ]
 [ 0.255833  0.2317    0.483333 ... 12.       30.        6.      ]
 [ 0.215833  0.223487  0.5775   ... 12.       31.        0.      ]]


# Create DataFrame with original data
inversed_df = pd.DataFrame(data = inversed, columns = num_var)
inversed_df

# Calculate based on UCI info to retrieve the actual temperature and weather information
#temp : Normalized temperature in Celsius. The values are divided to 41 (max)
#atemp: Normalized feeling temperature in Celsius. The values are divided to 50 (max)
#hum: Normalized humidity. The values are divided to 100 (max)
#windspeed: Normalized wind speed. The values are divided to 67 (max)
inversed_origin = pd.concat([inversed_df.iloc[:,0] * 41, #temperature
           inversed_df.iloc[:,1] * 50,                   #feeling temperature
           inversed_df.iloc[:,2] * 100,                  #humidity
           inversed_df.iloc[:,3] * 67],                  #wind speed
          axis=1)
inversed_origin


# timeseries of temp, atemp, humidity, windspeed
ax = inversed_origin.set_index(inversed_origin.index).plot(figsize=(20,5))
ax.set_xlabel('daily time (# rows)')

Text(0.5, 0, 'daily time (# rows)')


X_original = pd.concat([cat_df, inversed_origin, inversed_df[['month', 'day', 'dayofweek']]], axis=1)
X_original # Use this for SHAP plot view


# Create DataFrame with numerically scaled data
num_df = pd.DataFrame(data = numscaled, columns = num_var)
num_df


Xdf = pd.concat([cat_df, num_df], axis = 1)
print(Xdf.shape)
Xdf.head(5)

(731, 20)


# Save column names for SHAP usage
NAMES = Xdf.columns
NAMES

Index(['winter', 'spring', 'summer', 'fall', 'holiday_0', 'holiday_1',
       'workingday_0', 'workingday_1', 'clear', 'cloudy', 'lightsnow.rain',
       'year_2011', 'year_2012', 'temp', 'atemp', 'hum', 'windspeed', 'month',
       'day', 'dayofweek'],
      dtype='object')


plt.subplots(figsize=(8, 6))
sb.heatmap(Xdf.corr(), cmap='coolwarm')

<AxesSubplot: >


# Check if there is any non available
Xdf.isna().any().sum()

0


# Here, indicate which target to predict (cnt or casual or registered) 
# For the different models for each customer in the assignment, change the target 
to_predict = 'cnt'


# Here, indicate which target to predict (cnt or casual or registered) 
y = df[to_predict]

# Normalize target data: Calculate mean and standard deviation
y_mu = y.mean(0)
y_sd = y.std(0)

print('y mean:', y_mu, ' y std:' , y_sd)

y_norm = (y - y_mu) / y_sd
y_norm = y_norm.values.ravel() #dataframe to 1D array
#y_norm

y mean: 4504.3488372093025  y std: 1937.2114516187676


len(y)

731


# to verify, leave a whole dataframe with x and y together
tdf = pd.concat([Xdf, pd.DataFrame(y.values)], axis = 1)
tdf.rename(columns={0:'cnt'}, inplace=True) #change the column name
tdf


# Split the data into training, validation, and test data:
X_train, X_test, y_train, y_test = train_test_split(Xdf, y_norm, test_size = 0.2, random_state=1)
# Validation set
X_train, X_val, y_train, y_val = train_test_split(X_train, y_train, test_size=0.2, random_state=1)


# A parameter grid for XGBoost
params = {
        'min_child_weight': [1, 5, 10],
        'gamma': [0.5, 1, 1.5, 2, 5],
        'subsample': [0.6, 0.8, 1.0],
        'colsample_bytree': [0.6, 0.8, 1.0],
        'max_depth': [3, 4, 5]
        }


# Build the model with 
# XGBoost Training Parameter Reference: 
#   https://github.com/dmlc/xgboost
#   https://xgboost.readthedocs.io/en/latest/parameter.html

# XGBRegressor object and GridSearch object
xgbmodel = xgb.XGBRegressor() 
clf = GridSearchCV(estimator = xgbmodel,
                   param_grid = params,
                   cv = 3,
                   verbose=1)


# Fit the training data to the GridSearch object
t0 = time.time()
clf.fit(X_train, y_train)
t1 = time.time()
print(t1-t0, " sec taken")

Fitting 3 folds for each of 405 candidates, totalling 1215 fits
82.36391687393188  sec taken


# Display the best parameters from the GridSearch
# Let's assume that this is a good enough model to use for SHAP
print(clf.best_params_)
print(clf.best_score_)

{'colsample_bytree': 0.8, 'gamma': 0.5, 'max_depth': 4, 'min_child_weight': 5, 'subsample': 0.8}
0.8633448414950647


from sklearn._config import get_config, set_config
set_config(print_changed_only=False)


xgbmodel = xgb.XGBRegressor(**clf.best_params_, random_state=0)


# Fit the training data to the model
xgbmodel.fit(X_train, y_train, eval_set = [(X_train, y_train), (X_val, y_val)])

[0]	validation_0-rmse:0.84811	validation_1-rmse:0.86342
[1]	validation_0-rmse:0.67246	validation_1-rmse:0.69370
[2]	validation_0-rmse:0.54534	validation_1-rmse:0.58267
[3]	validation_0-rmse:0.45971	validation_1-rmse:0.50922
[4]	validation_0-rmse:0.40182	validation_1-rmse:0.45342
[5]	validation_0-rmse:0.36729	validation_1-rmse:0.40947
[6]	validation_0-rmse:0.33601	validation_1-rmse:0.38506
[7]	validation_0-rmse:0.31756	validation_1-rmse:0.37200
[8]	validation_0-rmse:0.30014	validation_1-rmse:0.36170
[9]	validation_0-rmse:0.28967	validation_1-rmse:0.35561
[10]	validation_0-rmse:0.28115	validation_1-rmse:0.34970
[11]	validation_0-rmse:0.27210	validation_1-rmse:0.34349
[12]	validation_0-rmse:0.27027	validation_1-rmse:0.33654
[13]	validation_0-rmse:0.26416	validation_1-rmse:0.33288
[14]	validation_0-rmse:0.25919	validation_1-rmse:0.33080
[15]	validation_0-rmse:0.25919	validation_1-rmse:0.33081
[16]	validation_0-rmse:0.25706	validation_1-rmse:0.32948
[17]	validation_0-rmse:0.25709	validation_1-rmse:0.32944
[18]	validation_0-rmse:0.25387	validation_1-rmse:0.32742
[19]	validation_0-rmse:0.25387	validation_1-rmse:0.32742
[20]	validation_0-rmse:0.24894	validation_1-rmse:0.32367
[21]	validation_0-rmse:0.24895	validation_1-rmse:0.32364
[22]	validation_0-rmse:0.24892	validation_1-rmse:0.32373
[23]	validation_0-rmse:0.24595	validation_1-rmse:0.32242
[24]	validation_0-rmse:0.24157	validation_1-rmse:0.32198
[25]	validation_0-rmse:0.24158	validation_1-rmse:0.32204
[26]	validation_0-rmse:0.24157	validation_1-rmse:0.32200
[27]	validation_0-rmse:0.24158	validation_1-rmse:0.32201
[28]	validation_0-rmse:0.24160	validation_1-rmse:0.32208
[29]	validation_0-rmse:0.23884	validation_1-rmse:0.31778
[30]	validation_0-rmse:0.23884	validation_1-rmse:0.31774
[31]	validation_0-rmse:0.23884	validation_1-rmse:0.31773
[32]	validation_0-rmse:0.23885	validation_1-rmse:0.31768
[33]	validation_0-rmse:0.23886	validation_1-rmse:0.31765
[34]	validation_0-rmse:0.23274	validation_1-rmse:0.31404
[35]	validation_0-rmse:0.23275	validation_1-rmse:0.31403
[36]	validation_0-rmse:0.23028	validation_1-rmse:0.30987
[37]	validation_0-rmse:0.23027	validation_1-rmse:0.30990
[38]	validation_0-rmse:0.23027	validation_1-rmse:0.30989
[39]	validation_0-rmse:0.23028	validation_1-rmse:0.30993
[40]	validation_0-rmse:0.23028	validation_1-rmse:0.30986
[41]	validation_0-rmse:0.23028	validation_1-rmse:0.30986
[42]	validation_0-rmse:0.23028	validation_1-rmse:0.30987
[43]	validation_0-rmse:0.23028	validation_1-rmse:0.30985
[44]	validation_0-rmse:0.23028	validation_1-rmse:0.30986
[45]	validation_0-rmse:0.23029	validation_1-rmse:0.30985
[46]	validation_0-rmse:0.23029	validation_1-rmse:0.30985
[47]	validation_0-rmse:0.23030	validation_1-rmse:0.30983
[48]	validation_0-rmse:0.23028	validation_1-rmse:0.30988
[49]	validation_0-rmse:0.23028	validation_1-rmse:0.30987
[50]	validation_0-rmse:0.23029	validation_1-rmse:0.30985
[51]	validation_0-rmse:0.23028	validation_1-rmse:0.30988
[52]	validation_0-rmse:0.23029	validation_1-rmse:0.30997
[53]	validation_0-rmse:0.23028	validation_1-rmse:0.30988
[54]	validation_0-rmse:0.23028	validation_1-rmse:0.30992
[55]	validation_0-rmse:0.23028	validation_1-rmse:0.30988
[56]	validation_0-rmse:0.23027	validation_1-rmse:0.30990
[57]	validation_0-rmse:0.23027	validation_1-rmse:0.30990
[58]	validation_0-rmse:0.23028	validation_1-rmse:0.30988
[59]	validation_0-rmse:0.23027	validation_1-rmse:0.30991
[60]	validation_0-rmse:0.23028	validation_1-rmse:0.30992
[61]	validation_0-rmse:0.22742	validation_1-rmse:0.30678
[62]	validation_0-rmse:0.22739	validation_1-rmse:0.30670
[63]	validation_0-rmse:0.22738	validation_1-rmse:0.30665
[64]	validation_0-rmse:0.22738	validation_1-rmse:0.30668
[65]	validation_0-rmse:0.22738	validation_1-rmse:0.30669
[66]	validation_0-rmse:0.22738	validation_1-rmse:0.30663
[67]	validation_0-rmse:0.22738	validation_1-rmse:0.30662
[68]	validation_0-rmse:0.22737	validation_1-rmse:0.30663
[69]	validation_0-rmse:0.22532	validation_1-rmse:0.30405
[70]	validation_0-rmse:0.22532	validation_1-rmse:0.30406
[71]	validation_0-rmse:0.22532	validation_1-rmse:0.30405
[72]	validation_0-rmse:0.22533	validation_1-rmse:0.30408
[73]	validation_0-rmse:0.22533	validation_1-rmse:0.30407
[74]	validation_0-rmse:0.22532	validation_1-rmse:0.30404
[75]	validation_0-rmse:0.22533	validation_1-rmse:0.30404
[76]	validation_0-rmse:0.22532	validation_1-rmse:0.30405
[77]	validation_0-rmse:0.22532	validation_1-rmse:0.30405
[78]	validation_0-rmse:0.22532	validation_1-rmse:0.30405
[79]	validation_0-rmse:0.22372	validation_1-rmse:0.30350
[80]	validation_0-rmse:0.22370	validation_1-rmse:0.30357
[81]	validation_0-rmse:0.22370	validation_1-rmse:0.30358
[82]	validation_0-rmse:0.22371	validation_1-rmse:0.30361
[83]	validation_0-rmse:0.22372	validation_1-rmse:0.30350
[84]	validation_0-rmse:0.22371	validation_1-rmse:0.30350
[85]	validation_0-rmse:0.22371	validation_1-rmse:0.30350
[86]	validation_0-rmse:0.22370	validation_1-rmse:0.30351
[87]	validation_0-rmse:0.22369	validation_1-rmse:0.30355
[88]	validation_0-rmse:0.22370	validation_1-rmse:0.30352
[89]	validation_0-rmse:0.22370	validation_1-rmse:0.30352
[90]	validation_0-rmse:0.22373	validation_1-rmse:0.30349
[91]	validation_0-rmse:0.22369	validation_1-rmse:0.30354
[92]	validation_0-rmse:0.22370	validation_1-rmse:0.30357
[93]	validation_0-rmse:0.22369	validation_1-rmse:0.30355
[94]	validation_0-rmse:0.22369	validation_1-rmse:0.30356
[95]	validation_0-rmse:0.22369	validation_1-rmse:0.30355
[96]	validation_0-rmse:0.22169	validation_1-rmse:0.30439
[97]	validation_0-rmse:0.22168	validation_1-rmse:0.30437
[98]	validation_0-rmse:0.22168	validation_1-rmse:0.30431
[99]	validation_0-rmse:0.22167	validation_1-rmse:0.30431

XGBRegressor(base_score=None, booster=None, callbacks=None,
             colsample_bylevel=None, colsample_bynode=None,
             colsample_bytree=0.8, early_stopping_rounds=None,
             enable_categorical=False, eval_metric=None, feature_types=None,
             gamma=0.5, gpu_id=None, grow_policy=None, importance_type=None,
             interaction_constraints=None, learning_rate=None, max_bin=None,
             max_cat_threshold=None, max_cat_to_onehot=None,
             max_delta_step=None, max_depth=4, max_leaves=None,
             min_child_weight=5, missing=nan, monotone_constraints=None,
             n_estimators=100, n_jobs=None, num_parallel_tree=None,
             objective='reg:squarederror', predictor=None, ...)

XGBRegressor(base_score=None, booster=None, callbacks=None,
             colsample_bylevel=None, colsample_bynode=None,
             colsample_bytree=0.8, early_stopping_rounds=None,
             enable_categorical=False, eval_metric=None, feature_types=None,
             gamma=0.5, gpu_id=None, grow_policy=None, importance_type=None,
             interaction_constraints=None, learning_rate=None, max_bin=None,
             max_cat_threshold=None, max_cat_to_onehot=None,
             max_delta_step=None, max_depth=4, max_leaves=None,
             min_child_weight=5, missing=nan, monotone_constraints=None,
             n_estimators=100, n_jobs=None, num_parallel_tree=None,
             objective='reg:squarederror', predictor=None, ...)


eval_result = xgbmodel.evals_result()
training_rounds = range(len(eval_result['validation_0']['rmse'])) #training
print(training_rounds)

range(0, 100)


plt.figure(figsize=(8,4))
plt.scatter(x=training_rounds,y=eval_result['validation_0']['rmse'],label='Training Error')
plt.scatter(x=training_rounds,y=eval_result['validation_1']['rmse'],label='Test Error')
plt.grid(True)
plt.xlabel('Iteration')
plt.ylabel('RMSE')
plt.title('Training Vs Test Error')
plt.legend()
plt.show()


ypredicted = xgbmodel.predict(X_test)


plt.figure(figsize=(10,3))
# Actual Vs Predicted
plt.plot(y_test, label='Actual')
plt.plot(ypredicted, label='Predicted')
plt.xlabel('Sample')
plt.ylabel('Count')
plt.xlim([100,150])
plt.title('Validation Dataset - Predicted Vs. Actual')
plt.legend()
plt.show()


# Over prediction and Under Prediction needs to be balanced
# Training Data Residuals
residuals = (y_test - ypredicted) # * y_sd + y_mu (for actual cnt numbers)

plt.figure(figsize=(10,3))
plt.hist(residuals)
plt.grid(True)
plt.xlabel('Actual - Predicted')
plt.ylabel('Count')
plt.title('Residuals Distribution')
plt.axvline(color='r')
plt.show()


print("RMSE: {0:0.4f}".format(mean_squared_error(y_test, ypredicted)))

RMSE: 0.0884


# Show the influencing features in order (with feature_importances_)
# Indicate which parameter used to decide the ranking of important features
important_features = pd.Series(data=xgbmodel.feature_importances_, index=NAMES)
important_features.sort_values(ascending=False,inplace=True)
print(important_features)

# There are different parameters:
# Refer to, https://github.com/dmlc/xgboost/blob/master/python-package/xgboost/core.py
#        * 'weight': the number of times a feature is used to split the data across all trees.
#        * 'gain': the average gain across all splits the feature is used in.
#        * 'cover': the average coverage across all splits the feature is used in.
#        * 'total_gain': the total gain across all splits the feature is used in.
#        * 'total_cover': the total coverage across all splits the feature is used in.

year_2012         0.334441
month             0.148238
year_2011         0.093973
atemp             0.091646
winter            0.060549
fall              0.057606
clear             0.034687
cloudy            0.033488
temp              0.031691
lightsnow.rain    0.028922
hum               0.022926
windspeed         0.017095
workingday_0      0.010901
holiday_0         0.009047
day               0.007691
dayofweek         0.007369
workingday_1      0.007166
spring            0.002563
holiday_1         0.000000
summer            0.000000
dtype: float32


important_features = pd.Series(data=xgbmodel.get_booster().get_score(importance_type='gain'), index=NAMES)
important_features.sort_values(ascending=False,inplace=True)
print(important_features)

year_2012         24.965616
month             11.065815
year_2011          7.015013
atemp              6.841238
winter             4.519948
fall               4.300218
clear              2.589336
cloudy             2.499859
temp               2.365730
lightsnow.rain     2.159016
hum                1.711424
windspeed          1.276122
workingday_0       0.813745
holiday_0          0.675331
day                0.574106
dayofweek          0.550102
workingday_1       0.534939
spring             0.191319
summer                  NaN
holiday_1               NaN
dtype: float64


# Show the influencing features in order (with get_booster().get_fscore())
# fscore is based on the number of times a feature is used in a tree
fscore = xgbmodel.get_booster().get_fscore().items()
lists = sorted(fscore, key=lambda x:x[1]) # sorted by value, return a list of tuples
fkey, fvalue = zip(*lists) # unpack a list of pairs into two tuples

plt.barh(fkey, fvalue)
plt.show()


# weekend and winter
sampled_at_hu = X_test[(X_test['workingday_0']==1) & (X_test['winter']==1)][:5]
sampled_at_hu


PICK = 364
Xdf[Xdf.index==PICK]


# review the target values, y[364] and tdf[tdf.index == 364]['cnt'].values[0] show target cnt = 2485
print(tdf[tdf.index == PICK]['cnt'].values[0])

2485


Xdf.iloc[[PICK]]


# Select one day for investigation with a force plot later
weekEnd_sample = Xdf.loc[[364]] #weekend-winter-364      #working day-winter-40
weekDay_sample = Xdf.loc[[180]] #working day-summer-180  #weekend-summer-247    

weekEnd_sample_origin = X_original.loc[[364]]
weekDay_sample_origin = X_original.loc[[180]]
weekEnd_sample_origin #weekDay_sample_origin


X_original.head(5)


# activate javascript, load javascript library when visualization is not showing
shap.initjs()

# explain all the predictions in the test set
t0 = time.time()
explainer = shap.TreeExplainer(xgbmodel)
shap_values = explainer.shap_values(Xdf)
t1 = time.time()
print(t1-t0, " sec taken")

0.0890500545501709  sec taken


selected_features = ["winter", "spring", "summer", "fall", "temp", "hum", "windspeed"]

# Find the indices of the selected features in the original DataFrame
feature_indices = [Xdf.columns.get_loc(feature) for feature in selected_features]

# Sort the SHAP values and DataFrame based on the feature indices
selected_shap_values = shap_values[:, feature_indices]
selected_Xdf = Xdf[selected_features]

# Create a SHAP summary plot for the selected features
shap.summary_plot(selected_shap_values, selected_Xdf, show=False, axis_color="black", plot_size=(20,5))

No data for colormapping provided via 'c'. Parameters 'vmin', 'vmax' will be ignored


selected_features = ["holiday_0", "holiday_1", "dayofweek"]

# Find the indices of the selected features in the original DataFrame
feature_indices = [Xdf.columns.get_loc(feature) for feature in selected_features]

# Sort the SHAP values and DataFrame based on the feature indices
selected_shap_values = shap_values[:, feature_indices]
selected_Xdf = Xdf[selected_features]

# Create a SHAP summary plot for the selected features
shap.summary_plot(selected_shap_values, selected_Xdf)

# Create a Dependence plot for the "dayofweek" feature
shap.dependence_plot("dayofweek", shap_values, X_original, feature_names=NAMES, show=False, interaction_index = None, title= "Dependence Plot of Day of Week")

No data for colormapping provided via 'c'. Parameters 'vmin', 'vmax' will be ignored


# Here, indicate which target to predict (casual) 
to_predict = 'casual'
y = df[to_predict]

# Normalize target data: Calculate mean and standard deviation
y_mu = y.mean(0)
y_sd = y.std(0)

y_norm = (y - y_mu) / y_sd
y_norm = y_norm.values.ravel() #dataframe to 1D array

# to verify, leave a whole dataframe with x and y together
tdf = pd.concat([Xdf, pd.DataFrame(y.values)], axis = 1)
tdf.rename(columns={0:'casusal'}, inplace=True) #change the column name

# Split the data into training, validation, and test data:
X_train, X_test, y_train, y_test = train_test_split(Xdf, y_norm, test_size = 0.2, random_state=1)
# Validation set
X_train, X_val, y_train, y_val = train_test_split(X_train, y_train, test_size=0.2, random_state=1) 

# A parameter grid for XGBoost
params = {
        'min_child_weight': [1, 5, 10],
        'gamma': [0.5, 1, 1.5, 2, 5],
        'subsample': [0.6, 0.8, 1.0],
        'colsample_bytree': [0.6, 0.8, 1.0],
        'max_depth': [3, 4, 5]
        }

# Build the model with 
# XGBRegressor object and GridSearch object
xgbmodel = xgb.XGBRegressor() 
clf = GridSearchCV(estimator = xgbmodel,
                   param_grid = params,
                   cv = 3,
                   verbose=1)

# Fit the training data to the GridSearch object
t0 = time.time()
clf.fit(X_train, y_train)
t1 = time.time()

from sklearn._config import get_config, set_config
set_config(print_changed_only=False)

xgbmodel = xgb.XGBRegressor(**clf.best_params_, random_state=0)

# Fit the training data to the model
xgbmodel.fit(X_train, y_train, eval_set = [(X_train, y_train), (X_val, y_val)], verbose=0)

# Get the RMSE
ypredicted = xgbmodel.predict(X_test)
print("RMSE (casual): {0:0.4f}".format(mean_squared_error(y_test, ypredicted)))

# activate javascript, load javascript library when visualization is not showing
shap.initjs()

# explain all the predictions in the test set
explainer_casual = shap.TreeExplainer(xgbmodel)
shap_values_casual = explainer_casual.shap_values(Xdf)

#########################################################################################################################################################
#########################################################################################################################################################
#########################################################################################################################################################

# Here, indicate which target to predict (registered) 
to_predict = 'registered'
y = df[to_predict]

# Normalize target data: Calculate mean and standard deviation
y_mu = y.mean(0)
y_sd = y.std(0)

y_norm = (y - y_mu) / y_sd
y_norm = y_norm.values.ravel() #dataframe to 1D array

# to verify, leave a whole dataframe with x and y together
tdf = pd.concat([Xdf, pd.DataFrame(y.values)], axis = 1)
tdf.rename(columns={0:'casusal'}, inplace=True) #change the column name

# Split the data into training, validation, and test data:
X_train, X_test, y_train, y_test = train_test_split(Xdf, y_norm, test_size = 0.2, random_state=1)
# Validation set
X_train, X_val, y_train, y_val = train_test_split(X_train, y_train, test_size=0.2, random_state=1) 

# A parameter grid for XGBoost
params = {
        'min_child_weight': [1, 5, 10],
        'gamma': [0.5, 1, 1.5, 2, 5],
        'subsample': [0.6, 0.8, 1.0],
        'colsample_bytree': [0.6, 0.8, 1.0],
        'max_depth': [3, 4, 5]
        }

# Build the model with 
# XGBRegressor object and GridSearch object
xgbmodel = xgb.XGBRegressor() 
clf = GridSearchCV(estimator = xgbmodel,
                   param_grid = params,
                   cv = 3,
                   verbose=1)

# Fit the training data to the GridSearch object
t0 = time.time()
clf.fit(X_train, y_train)
t1 = time.time()

from sklearn._config import get_config, set_config
set_config(print_changed_only=False)

xgbmodel = xgb.XGBRegressor(**clf.best_params_, random_state=0)

# Fit the training data to the model
xgbmodel.fit(X_train, y_train, eval_set = [(X_train, y_train), (X_val, y_val)], verbose=0)

# Get the RMSE
ypredicted = xgbmodel.predict(X_test)
print("RMSE (registered): {0:0.4f}".format(mean_squared_error(y_test, ypredicted)))

# activate javascript, load javascript library when visualization is not showing
shap.initjs()

# explain all the predictions in the test set
explainer_registered = shap.TreeExplainer(xgbmodel)
shap_values_registered = explainer_registered.shap_values(Xdf)

Fitting 3 folds for each of 405 candidates, totalling 1215 fits
RMSE (casual): 0.1744

Fitting 3 folds for each of 405 candidates, totalling 1215 fits
RMSE (registered): 0.1160


# Create a figure with two subplots side by side
fig, axes = plt.subplots(1, 2, figsize=(20, 5), facecolor='white')  # Set the facecolor

# Set the background color of each subplot
for ax in axes:
    ax.set_facecolor('white')
    
# Plot the first shap.dependence_plot on the first subplot
shap.dependence_plot(
    ("dayofweek"),
    shap_values_casual,
    X_original,
    feature_names=NAMES,
    show=False,
    interaction_index=None,
    ax=axes[0]  # Use the first subplot
)

# Set the title and its text color for the first subplot
axes[0].set_title("Day Of Weeks (Casual)", color='black', style='italic')

# Plot the second shap.dependence_plot on the second subplot
shap.dependence_plot(
    ("dayofweek"),
    shap_values_registered,
    X_original,
    feature_names=NAMES,
    show=False,
    interaction_index=None,
    ax=axes[1]  # Use the second subplot
)

# Set the title and its text color for the second subplot
axes[1].set_title("Day Of Weeks (Registered)", color='black', style='italic')

# Set the axis labels (x-axis and y-axis) text color
for ax in axes:
    ax.xaxis.label.set_color('black')
    ax.yaxis.label.set_color('black')
    
    # Set the color of the tick labels and tick marks
    ax.tick_params(axis='x', colors='black')
    ax.tick_params(axis='y', colors='black')

# Add a horizontal line at y=0.0
axes[0].axhline(y=0.0, color='red', linestyle='--')
axes[1].axhline(y=0.0, color='red', linestyle='--')

# Adjust spacing between subplots
plt.tight_layout()

# Show the plot
plt.show()


# Define the selected features
selected_features1 = ["winter", "spring", "summer", "fall", "atemp", "temp", "hum", "windspeed"]
selected_features2 = ["year_2011", "year_2012"]

# Create a grid of subplots for selected_features1 vs selected_features2
num_features1 = len(selected_features1)
num_features2 = len(selected_features2)

# Calculate SHAP interaction values
shap_interaction_values = explainer.shap_interaction_values(Xdf)

# Set up the subplot grid
fig, axes = plt.subplots(num_features1, num_features2, figsize=(20, 4 * num_features1))

# Loop through selected_features1 and selected_features2
for i in range(num_features1):
    for j in range(num_features2):
        feature1 = selected_features1[i]
        feature2 = selected_features2[j]
        feature_ = (feature1, feature2)

        # Create the dependence plot for the pair (feature1, feature2)
        shap.dependence_plot(
            feature_,
            shap_interaction_values, X_original,
            display_features=Xdf,
            ax=axes[i, j],
            show=False
        )
        
        # Set the title for the subplot
        axes[i, j].set_title(f"{feature1} vs. {feature2}", style="italic", weight='bold')

# Adjust spacing
plt.tight_layout()

# Show the combined plot
plt.show()


# Calculate SHAP interaction values
shap_interaction_values = explainer.shap_interaction_values(Xdf)

# Create a SHAP summary plot
shap.summary_plot(shap_interaction_values, Xdf, feature_names=NAMES, show=False)

# Get the current figure and axes
fig, axes = plt.gcf(), plt.gcf().get_axes()

# Set the background color for all columns
fig.patch.set_facecolor('white')
for ax in axes:
    ax.set_facecolor('white')

    # Set the title text color
    ax.set_title(ax.get_title(), color='black')

    # Set the x-label and y-label text color
    ax.xaxis.label.set_color('black')
    ax.yaxis.label.set_color('black')

    # Set the color of the tick labels and tick marks
    ax.tick_params(axis='x', colors='black')
    ax.tick_params(axis='y', colors='black')

# Show the plot
plt.show()

No data for colormapping provided via 'c'. Parameters 'vmin', 'vmax' will be ignored


tmp = np.abs(shap_interaction_values).mean(0)
for i in range(tmp.shape[0]):
    tmp[i,i] = 0
inds = np.argsort(-tmp.sum(0))[:50]
tmp2 = tmp[inds,:][:,inds]

# Set the figure size
plt.figure(figsize=(10, 10), facecolor='white')

# Create the heatmap with a different colormap
heatmap = plt.imshow(tmp2, cmap='rocket')

# Set the background
plt.gca().set_facecolor('white')

# Set the y-labels and x-labels
plt.ylabel('Features', color='black')
plt.xlabel('Features', color='black')

# Set the y-ticks and x-ticks
plt.yticks(range(tmp2.shape[0]), Xdf.columns.values[inds], rotation=50.4, horizontalalignment="right", color='black')
plt.xticks(range(tmp2.shape[0]), Xdf.columns.values[inds], rotation=50.4, horizontalalignment="left", color='black')

# Show the colorbar
cbar = plt.colorbar(heatmap)
# Set the color of colorbar ticks, labels, and scale numbers
cbar.ax.tick_params(colors='black')

# Set the color of the heatmap ticks
plt.tick_params(axis='both', colors='black')

# Set the x-axis ticks on top
plt.gca().xaxis.tick_top()

# Show the plot
plt.show()


# Create a figure with multiple subplots
fig, axes = plt.subplots(2, 3, figsize=(20, 10), facecolor='white')

# Set the background color of each subplot
for ax_row in axes:
    for ax in ax_row:
        ax.set_facecolor('white')

# Plot the first shap.dependence_plot on the first subplot
shap.dependence_plot(
    ("temp", "hum"),
    shap_interaction_values, X_original,
    ax=axes[0, 0],  # Use the first subplot
    show=False
)
axes[0, 0].set_title("Temperature vs. Humidity", color='black', style='italic', weight='bold')
axes[0, 0].xaxis.label.set_color('black')  # Set x-label text color
axes[0, 0].yaxis.label.set_color('black')  # Set y-label text color
axes[0, 0].tick_params(axis='x', colors='black')  # Set tick label color
axes[0, 0].tick_params(axis='y', colors='black')  # Set tick label color

# Plot the second shap.dependence_plot on the second subplot
shap.dependence_plot(
    ("hum", "year_2012"),
    shap_interaction_values, X_original,
    ax=axes[0, 1],  # Use the second subplot
    show=False
)
axes[0, 1].set_title("Humidity vs. Year 2012", color='black', style='italic', weight='bold')
axes[0, 1].xaxis.label.set_color('black')
axes[0, 1].yaxis.label.set_color('black')
axes[0, 1].tick_params(axis='x', colors='black')
axes[0, 1].tick_params(axis='y', colors='black')

# Plot the third shap.dependence_plot on the third subplot
shap.dependence_plot(
    ("atemp", "year_2011"),
    shap_interaction_values, X_original,
    ax=axes[0, 2],  # Use the third subplot
    show=False
)
axes[0, 2].set_title("Atemp vs. Year 2011", color='black', style='italic', weight='bold')
axes[0, 2].xaxis.label.set_color('black')
axes[0, 2].yaxis.label.set_color('black')
axes[0, 2].tick_params(axis='x', colors='black')
axes[0, 2].tick_params(axis='y', colors='black')

# Plot the fourth shap.dependence_plot on the fourth subplot
shap.dependence_plot(
    ("hum", "atemp"),
    shap_interaction_values, X_original,
    ax=axes[1, 0],  # Use the fourth subplot
    show=False
)
axes[1, 0].set_title("Humidity vs. Atemp", color='black', style='italic', weight='bold')
axes[1, 0].xaxis.label.set_color('black')
axes[1, 0].yaxis.label.set_color('black')
axes[1, 0].tick_params(axis='x', colors='black')
axes[1, 0].tick_params(axis='y', colors='black')

# Plot the fifth shap.dependence_plot on the fifth subplot
shap.dependence_plot(
    ("month", "year_2012"),
    shap_interaction_values, X_original,
    ax=axes[1, 2],  # Use the fifth subplot
    show=False
)
axes[1, 2].set_title("Month vs. Year 2012", color='black', style='italic', weight='bold')
axes[1, 2].xaxis.label.set_color('black')
axes[1, 2].yaxis.label.set_color('black')
axes[1, 2].tick_params(axis='x', colors='black')
axes[1, 2].tick_params(axis='y', colors='black')

# Remove the empty subplot in the last column
fig.delaxes(axes[1, 1])

# Access the color bars and set their color
for ax in axes.flatten():
    if ax.collections:
        colorbar = ax.collections[0].colorbar
        colorbar.ax.yaxis.label.set_color('black')  # Set color bar label text color
        colorbar.ax.tick_params(axis='y', colors='black')  # Set color bar tick label color

# Adjust spacing between subplots
plt.tight_layout()

# Show the plot
plt.show()


selected_features = ["day", "month", "year_2011", "year_2012"]

# Find the indices of the selected features in the original DataFrame
feature_indices = [Xdf.columns.get_loc(feature) for feature in selected_features]

# Sort the SHAP values and DataFrame based on the feature indices
selected_shap_values = shap_values[:, feature_indices]
selected_Xdf = Xdf[selected_features]

# Create a SHAP summary plot for the selected features
shap.summary_plot(selected_shap_values, selected_Xdf, show=False, axis_color="black", plot_size=(20,5))

# Create a figure with three subplots side by side
fig, axes = plt.subplots(1, 2, figsize=(20, 5))

# Create the first dependence plot for "day"
shap.dependence_plot("day", shap_values, X_original, feature_names=NAMES, show=False, interaction_index=None, ax=axes[0], title="Dependence Plot of Day")

# Add a shaded line at y-axis zero
axes[0].axhline(y=0, color="red", linestyle="--", alpha=0.7)

# Create the second dependence plot for "month"
shap.dependence_plot("month", shap_values, X_original, feature_names=NAMES, show=False, interaction_index=None, ax=axes[1], title="Dependence Plot of Month")

# Add a shaded line at y-axis zero
axes[1].axhline(y=0, color="red", linestyle="--", alpha=0.7)

plt.tight_layout()  # Adjust subplot spacing
plt.show()  # Display the combined plot

No data for colormapping provided via 'c'. Parameters 'vmin', 'vmax' will be ignored

	count	mean	std	min	25%	50%	75%	max
instant	731.0	366.000000	211.165812	1.000000	183.500000	366.000000	548.500000	731.000000
season	731.0	2.496580	1.110807	1.000000	2.000000	3.000000	3.000000	4.000000
yr	731.0	0.500684	0.500342	0.000000	0.000000	1.000000	1.000000	1.000000
mnth	731.0	6.519836	3.451913	1.000000	4.000000	7.000000	10.000000	12.000000
holiday	731.0	0.028728	0.167155	0.000000	0.000000	0.000000	0.000000	1.000000
weekday	731.0	2.997264	2.004787	0.000000	1.000000	3.000000	5.000000	6.000000
workingday	731.0	0.683995	0.465233	0.000000	0.000000	1.000000	1.000000	1.000000
weathersit	731.0	1.395349	0.544894	1.000000	1.000000	1.000000	2.000000	3.000000
temp	731.0	0.495385	0.183051	0.059130	0.337083	0.498333	0.655417	0.861667
atemp	731.0	0.474354	0.162961	0.079070	0.337842	0.486733	0.608602	0.840896
hum	731.0	0.627894	0.142429	0.000000	0.520000	0.626667	0.730209	0.972500
windspeed	731.0	0.190486	0.077498	0.022392	0.134950	0.180975	0.233214	0.507463
casual	731.0	848.176471	686.622488	2.000000	315.500000	713.000000	1096.000000	3410.000000
registered	731.0	3656.172367	1560.256377	20.000000	2497.000000	3662.000000	4776.500000	6946.000000
cnt	731.0	4504.348837	1937.211452	22.000000	3152.000000	4548.000000	5956.000000	8714.000000
year	731.0	2011.500684	0.500342	2011.000000	2011.000000	2012.000000	2012.000000	2012.000000
month	731.0	6.519836	3.451913	1.000000	4.000000	7.000000	10.000000	12.000000
day	731.0	15.738714	8.809949	1.000000	8.000000	16.000000	23.000000	31.000000
dayofweek	731.0	3.002736	2.004787	0.000000	1.000000	3.000000	5.000000	6.000000

	temp	atemp	hum	windspeed
0	14.110847	18.18125	80.5833	10.749882
1	14.902598	17.68695	69.6087	16.652113
2	8.050924	9.47025	43.7273	16.636703
3	8.200000	10.60610	59.0435	10.739832
4	9.305237	11.46350	43.6957	12.522300
...	...	...	...	...
726	10.420847	11.33210	65.2917	23.458911
727	10.386653	12.75230	59.0000	10.416557
728	10.386653	12.12000	75.2917	8.333661
729	10.489153	11.58500	48.3333	23.500518
730	8.849153	11.17435	57.7500	10.374682

	temp	atemp	hum	windspeed	month	day	dayofweek
0	-0.826662	-0.679946	1.250171	-0.387892	-1.600161	-1.674108	0.996930
1	-0.721095	-0.740652	0.479113	0.749602	-1.600161	-1.560522	1.496077
2	-1.634657	-1.749767	-1.339274	0.746632	-1.600161	-1.446936	-1.498809
3	-1.614780	-1.610270	-0.263182	-0.389829	-1.600161	-1.333351	-0.999661
4	-1.467414	-1.504971	-1.341494	-0.046307	-1.600161	-1.219765	-0.500513
...	...	...	...	...	...	...	...
726	-1.318665	-1.521108	0.175807	2.061426	1.588660	1.279122	-0.001366
727	-1.323224	-1.346690	-0.266238	-0.452131	1.588660	1.392707	0.497782
728	-1.323224	-1.424344	0.878392	-0.853552	1.588660	1.506293	0.996930
729	-1.309558	-1.490049	-1.015664	2.069444	1.588660	1.619879	1.496077
730	-1.528225	-1.540482	-0.354061	-0.460201	1.588660	1.733465	-1.498809

Contents¶

Setup and overview¶

Notice: We select meaningful variables for a better understanding of the SHAP results.¶

We will use 'dayofweek' variable when describing each day of a week. Weekends of Saturday and Sunday are 5 and 6. The other weekdays are 0 to 4.¶

Workingday feature is a boolean.¶

Note that season 1 indicates winter. Do you agree?¶

Data cleaning¶

Categorical variables¶

Numerical variables¶

Grid search and modeling¶

Train with best parameters¶

Predict¶

Check which single instances to compare with LIME¶

Explain model with SHAP¶

Questions and Answers¶

	instant	season	yr	mnth	holiday	weekday	workingday	weathersit	temp	atemp	hum	windspeed	casual	registered	cnt	year	month	day	dayofweek
dteday
2011-01-01	1	1	0	1	0	6	0	2	0.344167	0.363625	0.805833	0.160446	331	654	985	2011	1	1	5
2011-01-02	2	1	0	1	0	0	0	2	0.363478	0.353739	0.696087	0.248539	131	670	801	2011	1	2	6
2011-01-03	3	1	0	1	0	1	1	1	0.196364	0.189405	0.437273	0.248309	120	1229	1349	2011	1	3	0

	season_1	season_2	season_3	season_4	holiday_0	holiday_1	workingday_0	workingday_1	weathersit_1	weathersit_2	weathersit_3	year_2011	year_2012
0	1.0	0.0	0.0	0.0	1.0	0.0	1.0	0.0	0.0	1.0	0.0	1.0	0.0
1	1.0	0.0	0.0	0.0	1.0	0.0	1.0	0.0	0.0	1.0	0.0	1.0	0.0
2	1.0	0.0	0.0	0.0	1.0	0.0	0.0	1.0	1.0	0.0	0.0	1.0	0.0
3	1.0	0.0	0.0	0.0	1.0	0.0	0.0	1.0	1.0	0.0	0.0	1.0	0.0
4	1.0	0.0	0.0	0.0	1.0	0.0	0.0	1.0	1.0	0.0	0.0	1.0	0.0
...	...	...	...	...	...	...	...	...	...	...	...	...	...
726	1.0	0.0	0.0	0.0	1.0	0.0	0.0	1.0	0.0	1.0	0.0	0.0	1.0
727	1.0	0.0	0.0	0.0	1.0	0.0	0.0	1.0	0.0	1.0	0.0	0.0	1.0
728	1.0	0.0	0.0	0.0	1.0	0.0	1.0	0.0	0.0	1.0	0.0	0.0	1.0
729	1.0	0.0	0.0	0.0	1.0	0.0	1.0	0.0	1.0	0.0	0.0	0.0	1.0
730	1.0	0.0	0.0	0.0	1.0	0.0	0.0	1.0	0.0	1.0	0.0	0.0	1.0

	winter	spring	summer	fall	holiday_0	holiday_1	workingday_0	workingday_1	clear	cloudy	lightsnow.rain	year_2011	year_2012
0	1.0	0.0	0.0	0.0	1.0	0.0	1.0	0.0	0.0	1.0	0.0	1.0	0.0
1	1.0	0.0	0.0	0.0	1.0	0.0	1.0	0.0	0.0	1.0	0.0	1.0	0.0
2	1.0	0.0	0.0	0.0	1.0	0.0	0.0	1.0	1.0	0.0	0.0	1.0	0.0
3	1.0	0.0	0.0	0.0	1.0	0.0	0.0	1.0	1.0	0.0	0.0	1.0	0.0
4	1.0	0.0	0.0	0.0	1.0	0.0	0.0	1.0	1.0	0.0	0.0	1.0	0.0
...	...	...	...	...	...	...	...	...	...	...	...	...	...
726	1.0	0.0	0.0	0.0	1.0	0.0	0.0	1.0	0.0	1.0	0.0	0.0	1.0
727	1.0	0.0	0.0	0.0	1.0	0.0	0.0	1.0	0.0	1.0	0.0	0.0	1.0
728	1.0	0.0	0.0	0.0	1.0	0.0	1.0	0.0	0.0	1.0	0.0	0.0	1.0
729	1.0	0.0	0.0	0.0	1.0	0.0	1.0	0.0	1.0	0.0	0.0	0.0	1.0
730	1.0	0.0	0.0	0.0	1.0	0.0	0.0	1.0	0.0	1.0	0.0	0.0	1.0

	winter	holiday_0	workingday_0	clear	cloudy	year_2011	year_2012	temp	atemp	hum	windspeed	month	day	dayofweek
8	1.0	1.0	1.0	1.0	0.0	1.0	0.0	-1.951895	-2.199446	-1.361096	2.214012	-1.600161	-0.765422	1.496077
364	1.0	1.0	1.0	1.0	0.0	1.0	0.0	-0.466773	-0.369869	-0.084739	0.383083	1.588660	1.733465	0.996930
414	1.0	1.0	1.0	0.0	1.0	0.0	1.0	-1.177444	-1.280727	-0.787324	0.808650	-1.310268	0.370436	1.496077
434	1.0	1.0	1.0	1.0	0.0	0.0	1.0	-1.136444	-1.207168	-1.949511	0.455341	-1.020375	-0.651836	0.996930
421	1.0	1.0	1.0	1.0	0.0	0.0	1.0	-1.181998	-1.265253	-1.530890	0.190365	-1.310268	1.165536	1.496077

	season_1	season_2	season_3	season_4	holiday_0	holiday_1	workingday_0	workingday_1	weathersit_1	weathersit_2	weathersit_3	year_2011	year_2012
0	1.0	0.0	0.0	0.0	1.0	0.0	1.0	0.0	0.0	1.0	0.0	1.0	0.0
1	1.0	0.0	0.0	0.0	1.0	0.0	1.0	0.0	0.0	1.0	0.0	1.0	0.0
2	1.0	0.0	0.0	0.0	1.0	0.0	0.0	1.0	1.0	0.0	0.0	1.0	0.0
3	1.0	0.0	0.0	0.0	1.0	0.0	0.0	1.0	1.0	0.0	0.0	1.0	0.0
4	1.0	0.0	0.0	0.0	1.0	0.0	0.0	1.0	1.0	0.0	0.0	1.0	0.0
...	...	...	...	...	...	...	...	...	...	...	...	...	...
726	1.0	0.0	0.0	0.0	1.0	0.0	0.0	1.0	0.0	1.0	0.0	0.0	1.0
727	1.0	0.0	0.0	0.0	1.0	0.0	0.0	1.0	0.0	1.0	0.0	0.0	1.0
728	1.0	0.0	0.0	0.0	1.0	0.0	1.0	0.0	0.0	1.0	0.0	0.0	1.0
729	1.0	0.0	0.0	0.0	1.0	0.0	1.0	0.0	1.0	0.0	0.0	0.0	1.0
730	1.0	0.0	0.0	0.0	1.0	0.0	0.0	1.0	0.0	1.0	0.0	0.0	1.0

	winter	spring	summer	fall	holiday_0	holiday_1	workingday_0	workingday_1	clear	cloudy	lightsnow.rain	year_2011	year_2012
0	1.0	0.0	0.0	0.0	1.0	0.0	1.0	0.0	0.0	1.0	0.0	1.0	0.0
1	1.0	0.0	0.0	0.0	1.0	0.0	1.0	0.0	0.0	1.0	0.0	1.0	0.0
2	1.0	0.0	0.0	0.0	1.0	0.0	0.0	1.0	1.0	0.0	0.0	1.0	0.0
3	1.0	0.0	0.0	0.0	1.0	0.0	0.0	1.0	1.0	0.0	0.0	1.0	0.0
4	1.0	0.0	0.0	0.0	1.0	0.0	0.0	1.0	1.0	0.0	0.0	1.0	0.0
...	...	...	...	...	...	...	...	...	...	...	...	...	...
726	1.0	0.0	0.0	0.0	1.0	0.0	0.0	1.0	0.0	1.0	0.0	0.0	1.0
727	1.0	0.0	0.0	0.0	1.0	0.0	0.0	1.0	0.0	1.0	0.0	0.0	1.0
728	1.0	0.0	0.0	0.0	1.0	0.0	1.0	0.0	0.0	1.0	0.0	0.0	1.0
729	1.0	0.0	0.0	0.0	1.0	0.0	1.0	0.0	1.0	0.0	0.0	0.0	1.0
730	1.0	0.0	0.0	0.0	1.0	0.0	0.0	1.0	0.0	1.0	0.0	0.0	1.0

	season_1	season_2	season_3	season_4	holiday_0	holiday_1	workingday_0	workingday_1	weathersit_1	weathersit_2	weathersit_3	year_2011	year_2012
0	1.0	0.0	0.0	0.0	1.0	0.0	1.0	0.0	0.0	1.0	0.0	1.0	0.0
1	1.0	0.0	0.0	0.0	1.0	0.0	1.0	0.0	0.0	1.0	0.0	1.0	0.0
2	1.0	0.0	0.0	0.0	1.0	0.0	0.0	1.0	1.0	0.0	0.0	1.0	0.0
3	1.0	0.0	0.0	0.0	1.0	0.0	0.0	1.0	1.0	0.0	0.0	1.0	0.0
4	1.0	0.0	0.0	0.0	1.0	0.0	0.0	1.0	1.0	0.0	0.0	1.0	0.0
...	...	...	...	...	...	...	...	...	...	...	...	...	...
726	1.0	0.0	0.0	0.0	1.0	0.0	0.0	1.0	0.0	1.0	0.0	0.0	1.0
727	1.0	0.0	0.0	0.0	1.0	0.0	0.0	1.0	0.0	1.0	0.0	0.0	1.0
728	1.0	0.0	0.0	0.0	1.0	0.0	1.0	0.0	0.0	1.0	0.0	0.0	1.0
729	1.0	0.0	0.0	0.0	1.0	0.0	1.0	0.0	1.0	0.0	0.0	0.0	1.0
730	1.0	0.0	0.0	0.0	1.0	0.0	0.0	1.0	0.0	1.0	0.0	0.0	1.0

	winter	spring	summer	fall	holiday_0	holiday_1	workingday_0	workingday_1	clear	cloudy	lightsnow.rain	year_2011	year_2012
0	1.0	0.0	0.0	0.0	1.0	0.0	1.0	0.0	0.0	1.0	0.0	1.0	0.0
1	1.0	0.0	0.0	0.0	1.0	0.0	1.0	0.0	0.0	1.0	0.0	1.0	0.0
2	1.0	0.0	0.0	0.0	1.0	0.0	0.0	1.0	1.0	0.0	0.0	1.0	0.0
3	1.0	0.0	0.0	0.0	1.0	0.0	0.0	1.0	1.0	0.0	0.0	1.0	0.0
4	1.0	0.0	0.0	0.0	1.0	0.0	0.0	1.0	1.0	0.0	0.0	1.0	0.0
...	...	...	...	...	...	...	...	...	...	...	...	...	...
726	1.0	0.0	0.0	0.0	1.0	0.0	0.0	1.0	0.0	1.0	0.0	0.0	1.0
727	1.0	0.0	0.0	0.0	1.0	0.0	0.0	1.0	0.0	1.0	0.0	0.0	1.0
728	1.0	0.0	0.0	0.0	1.0	0.0	1.0	0.0	0.0	1.0	0.0	0.0	1.0
729	1.0	0.0	0.0	0.0	1.0	0.0	1.0	0.0	1.0	0.0	0.0	0.0	1.0
730	1.0	0.0	0.0	0.0	1.0	0.0	0.0	1.0	0.0	1.0	0.0	0.0	1.0